Saudi Cultural Missions Theses & Dissertations
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Item Restricted The Application of Blockchains to Railway Condition Monitoring(University of Birmingham, 2025-05) Alzahrani, Rahma A; Easton, John MAgeing infrastructure and fragmented data ownership present major challenges to remote condition monitoring technologies in the European railway sector. Despite the potential of these technologies to improve efficiency and safety, their deployment is often limited by issues related to data silos, stakeholder mistrust, and the lack of transparent, enforceable cost attribution models. This thesis investigates how blockchain and smart contract technologies can be leveraged to address these challenges. The research focuses on key questions: how blockchain can reduce centralisation and mistrust; how it can improve transparency and compliance in data cost attribution; how smart contracts can automate and streamline the attribution process; how blockchain can ensure data integrity without storing large volumes of data; and what practical applications blockchain may have in railway operations. A blockchain-based framework was designed and implemented to enable fair, transparent, and legally compliant attribution of data costs across stakeholders. The system incorporates smart contracts to enforce agreement clauses without third-party involvement. The performance of the developed framework was tested under various scenarios to assess scalability, execution efficiency, and compliance with railway sector requirements. The primary contributions of this research are: the development of a cross-border data accounting framework; the establishment of operational links between the framework and real-world business and commercial processes; and a working proof-of-concept tailored to the European rail industry. These contributions demonstrate that blockchain can serve as a practical and scalable foundation for trusted, decentralised data management in multi-stakeholder transport environments.11 0Item Restricted Travel Efficiency Investigation: Unravelling Local and Global Insights via Taxi Trajectory Analysis(RMIT University, 2024-07) Alshikhe, Rania; Harland, JamesTransportation issues have a significant impact on people's lives because they spend a significant amount of time commuting for either daily needs or entertainment. These issues can be associated with travel time, longer travel distance, and/or fuel consumption. Due to the global positioning system (GPS) enabled devices installed in these vehicles, enormous amounts of trajectory data have been collected over the last decade from travelling vehicles such as cars, buses, and taxis, among others. This data provides an excellent opportunity to trace vehicle movements in fine spatiotemporal granularity. Moreover, this data tackles many of the traffic problems, including bottleneck identification. Identifying traffic bottlenecks is essential in traffic planning it also aids in the prevention of traffic congestion. Traffic congestion begins with congested road segments in key locations and spreads to other parts of the urban road network, causing additional congestion. The problem investigated in this thesis is analysing the road network travel efficiency locally and globally to reduce travel times, minimising fuel consumption, energy demands, and making better use of existing infrastructure. In much of the current literature, the focus is often on either a global analysis, which identifies the most efficient trip destinations, or a local analysis, which identifies the cause of traffic anomalies or congestion. However, it is necessary to consider both of these scales in order to gain a nuanced understanding. Specifically, it is crucial to quantify the extent to which each individual road segment affects travel efficiency, both at a local and a global scale. In order to provide a comprehensive understanding of urban traffic data, this thesis integrates both local and global analyses. In local analysis, we dive deep into each trajectory, much like deep-sea exploration, to uncover reasons for inefficiencies by examining all combined road segments. Then we extend the analysis globally to understand the behaviour of each part on road networks and how it effects on other road parts. The local analysis of the road network explores the measuring of the travel efficiency for each single trajectory trip across numerous origin-destination (OD) pairs in an entire city. Moreover, the consideration of a low travel efficiency path rises a 1 question of exactly which road segment is causing low efficiency. So, local analysis aims to measure the travel efficiency for each path. Furthermore, the local analysis provides the road segment inside a particular path that is responsible for low travel efficiency. In contrast, a small set of road segments that affect globally in the congestion problem is known as global analysis in this thesis. The global analysis seeks to identify a major source of traffic congestion. The global analysis provides some important opportunities for furthering the understanding of the congestion value for each edge in the road network and provides the top-k congested edges that influence the greatest number of other edges in the road network with the highest influence value recorded. The highest number of the influence values proves evidence of the global congestion effect in the entire road network.26 0Item Restricted SMART AUTHENTICATION MECHANISMS: UTILIZING BIG DATA FOR DYNAMIC AND PERSONALIZED SECURITY SOLUTIONS(The University of Western Ontario, 2024-08-25) Abu Sulayman, Iman; Ouda, AbdelkaderThe exponential growth of digital data is revolutionizing information security and reshaping defense strategies against unknown threats. Organizations are amassing vast amounts of personal data, collectively termed ”Big Data,” from various sources like social media, online transactions, and GPS signals. This surge in data presents new research challenges in information security, prompting organizations to leverage big data analytics for valuable insights within secure environments. As a result, organizations are redesigning network security protocols to effectively manage the characteristics of big data. While traditional research focuses on authenticating users to protect big data environments, an alternative perspective emerges: utilizing big data to raise a new generation of authentication mechanisms to safeguard other environments. To this end, we developed novel security solutions that harness big data analytics to generate unique patterns of users’ dynamic behaviors, enabling the design of smart knowledge-based authentication mechanisms to fulfill the requirements of the new era of the digital world. These solutions include three main modules. ”Data Security-based Analytics (DSA),” the first module, develops an innovative data transformation model. The model adapts big data’s characteristics to relevant human dynamic measures. The second module, known as ”Big Data Driven Authentication (BDA),” includes the Security User Profiles (SUP) creation model, which is responsible for identifying patterns in DSA’s output and then uses said patterns to detect legitimate but anomalous activity from the user and assemble a security profile about the user. BDA also includes another model, known as Just-in-time Human Dynamics-Based Authentication Engine (JitHDA), which uses the user’s security profiles to dynamically create secure challenge questions in real-time that derive from the user’s recent behavior. The third module describes the development of a novel “Big Data-Driven Authentication as a Service (AUTHaaS)” model. AUTHaaS is an authentication mechanism that is powered by SUP and JitHDA technologies to offer authentication services on the cloud. Another model in AUTHaaS is ”iAuth,” which is an integration framework for authentication services. We developed this model to offer a unified interface that enables collaboration and interoperability among various AUTHaaS service providers. Additionally, we have developed an algorithm-based data generation (ADG) engine that is capable of processing synthetic user data. We designed ADG to accommodate dual-mode user behavioral data, encompassing both normal and abnormal instances. More importantly, the engine does not necessitate an initial dataset or data distribution and serves as the dataset source for the DSA model as it generates data from five different application domains.7 0Item Restricted Creating value using Big Data applications in complex projects: a systematic review of the construction sector in a risk management perspective(Saudi Digital Library, 2023-09-04) Yamour, Jenaideb S; Qazi, KamalThe study delineates several key objectives: a comprehensive exploration of Big Data's integration in the sector; an assessment of its merits and challenges; a historical mapping of its evolution; and proffering trajectories for future scholarly and practical endeavours. It underscores the inherent inadequacy of conventional risk assessment tools, particularly for contemporary construction undertakings characterized by intricate designs and stringent timelines, emphasizing the revolutionary potential of Big Data in bolstering industry resilience and predictive prowess. The methodology underpinning this research is anchored in a systemic literature review, aiming to holistically encapsulate the extant body of knowledge on the subject. Pivotal inquiries driving this investigation include the value-addition of Big Data in construction risk management, and its interplay with project complexity. A methodological flowchart shows the research's steps. Key revelations from this investigation points up the reputation of Big Data-centric technologies in risk detection and mitigation throughout construction phases. Techniques like Monte Carlo simulations using Big Data, employing probabilistic assessments for diverse scenarios, have gained prominence. Furthermore, Building Information Modelling (BIM) leverages Big Data for enhanced design fidelity, minimizing design-associated risks. The research also highlights the potency of the MapReduce Hadoop programming paradigm in fortifying risk identification and management. The study also sheds light on Big Data's instrumental role in improving the occupational environment for construction personnel. Conclusively, the paper clarifies the expanding potential of Big Data in refining construction processes, risk mitigating, and bolstering the efficacy and foresight of project management. In essence, this review offers a holistic perspective on Big Data's role in the construction sector's risk management, enhancing existing literature through the discussion of contemporary frameworks. The insights garnered will undoubtedly prove invaluable to researchers and industry practitioners keen on refining risk management strategies through Big Data integrations29 0Item Restricted Pattern Recognition & Predictive Analysis of Cardiovascular Diseases: A Machine Learning Approach(Saudi Digital Library, 2023-11-23) Alseraihi, Faisal Fahad; Naich, AmmarCardiovascular disease (CVD) is a predominant global health concern, with its impact becoming increasingly pronounced in low- and middle- income countries due to challenges like limited healthcare access, inadequate public awareness, and lifestyle-related risks. Addressing CVD's multifactorial origins, which span genetic, environmental, and behavioral domains, requires advanced diagnostic techniques. This research leverages the UCI Heart Disease dataset to develop a deep learning predictive model for CVD, incorporating 14 vital heart health parameters. The models performance is critically assessed against conventional machine learning approaches, shedding light on its efficiency and areas of refinement. Utilizing sophisticated Neural Network structures, this study strives to enhance predictive health analytics, aiming for timely CVD identification and intervention. As the integration of machine learning into healthcare deepens, it's crucial to ensure that these tools are robust, thoroughly evaluated, and augment clinical insights to reduce misdiagnosis risks.69 0Item Restricted Algorithm And Design Tool Development Of Sizing A Stand-Alone PV/WT/Electrolyzer/Hydrogen/Fuel-cell Power Generation System For Electricity Supply Under Weather related Uncertainty Considerations And Thermo-Fluid Effects(PreQuest, 2023-09-22) Alfulayyih, Yasir Mohammed; Li, PeiwenToday, reducing the greenhouse gas emissions and decentralizing the electrical power plants has become a worldwide target, which, in turns, helps in avoiding causing any damage to the environment and increasing and easing the accessibility to the network, respectively, especially at remote areas. One of the most promising ways for achieving this target is the utilization of green and renewable energy resources (RER) such as solar energy (SE) and wind energy (WE). However, in nature, SE and WE suffer from the intermittence and variability (IV) (also called volatility), which have made the task of forecasting the power outputs from and designing/sizing a RE-based power plant (REPP) very challenging and have made the reliability to become a big source of concern for the decisions makers. Nonetheless, researchers have been attempting to develop different approaches for the purpose of utilizing these RER and mitigating this natural issue as much as possible, simultaneously. One of the most promising and appreciated mitigator for the IV effect is by hybridizing the power plants of SE and WE (H-REPP), with not or equal penetration levels, by integrating the H-REPP with an energy storage system (ESS) (e.g., compressed air, pumped hydro (PHS), regenerative fuel cell (RFC), etc.), and by using a method that can forecast the historical weather data (called WYGM from now onward) for the sake of obtaining any statistical data that can map the potential at any site of interests. Moreover, since it is impractical to request unlimited surface area and energy storage capacity (ESC) for harvesting and storage SE/WE, respectively, but to be reasonable via developing sizing algorithms, instead; this has been introduced in the literature under the topic of sizing REPP. However, usually, in the literature, these three tasks (modeling H-REPP, formulating WYGMs, and developing sizing algorithms) are separately studied/adopted, which should not be the case due to the strong connections among these tasks; additionally, the literature is lacking from modeling and sizing algorithms for H-REPP where the above-mentioned complexity is addressed. Therefore, in this work, the core goal is to estimate the least required size of a H-REPP that can operate for a year-round, according to different weather conditions, and consists of: solar photovoltaic power generation system (SPVPGS) (thus, the concentrated solar panel (CSP) are excluded) and wind turbine power generation system (WTPGS) for direct power supply and RFC for indirect power supply and/or hydrogen production (called green hydrogen production system). This core goal has been divided into a set of objectives as follows: 1) building an advanced modeling of SPVPGS and WTGPS, 2) designing a novel WYGM that efficiently involve the IV effect and fulfills the requirements of such H-REPP at any particular site of interest and along with a new spatial-temporal weighting (STW) approach, 3) developing an algorithm for sizing such kind of H-REPP along with a RFC as function of the supply-demand period, 4). Optimizing the penetration levels of SE-WE according to the estimated potential of the location; also, improve the possible sub-optimal results by decreasing the size and the updating step. Additionally, this conducted research aims at: studying the effect of the initial conditions for the sake of selecting the “best day of start energy harvest and storage (BDHS), proposing a new method of determining the most-frequent data point for a data set that has a strongly varied standard deviation from time to time, considering the need for updating the surface roughness with wind direction inside the simulation of WTPGS, and implementing the entire work into a software. These objectives have been planned to be achieved in four phases: I. simple SPVPGS-RFC and WYGM, II. advanced SPVPGS-WTPGS and simple RFC and semi-advanced WYGM, III. advanced SPVPGS-WTPGS-RFC (and other forms of ESS as possible) and advanced WYGM. IV. releasing of the software (called renewable energy power system sizing software (REPPS)). The obtained results have showed the following. First, the hybridization of multiple resources (i.e., PV-WT) have reduced the required size of power plant, which has proven that such solution can minimize the intermittency effect and then, eventually, increase the reliability. Second, investigating and considering more than one statical type of weather years (i.e., average, most-frequent, worst, and best) has led to a major and significant finding, but not limited to, which is that Considering only one type is not recommended due to the huge variation on the harvested energy among all the types. Moreover, the novel developed generation methodology for weather year data set, in general, and the most-frequent data point selection method, in particular, have showed a very promising solution along with an acceptable range of errors. Also, it has been validated and showed that its performance is capable of predicting the limits of the highest and lowest possible of energy harvesting and supply. Additionally, the effect of different miscellaneous factors (i.e., tracking systems, round-trip efficiency of different energy storage systems, model-related uncertainty, etc.) have been investigated. Among the major and unique results that have been attained using the developed algorithm (REPPS) and weather year data set generator (ABTMY(SITY)) is the estimated range of uncertainty for the four modes; and, based on the validation, the tested day for a year-round operation and based on a real-new data set, the required area and energy storage capacity have both fallen within the estimated range. Future work can be conducted to study the effect of the environment on the performance of the RFC, consider the cooling and shading effects on the performance of the SPVPGS, apply the STW to the developed WYGM, and optimize the levels of penetration by the SE and WE at the studied site and selected conditions.34 0